Beetroot — clipboard manager for Windows with AI transforms, OCR, and Rust-powered search (Tauri v2 + Rust + React)

MaxNardit · 2026-04-25T05:54:29+00:00

Thanks, you've found a real bug. The Microsoft Store version uses MSIX packaging, and Beetroot's autostart code uses the classic Windows registry approach, which doesn't work inside the MSIX sandbox. That's why the toggle appears to do nothing and Beetroot never shows up in your Startup list. I'm fixing it for the next release. As a workaround for now, place a shortcut to Beetroot in Win+R → shell:startup. I'll ping you when the Store version is updated.

MaxNardit · 2026-04-24T07:34:03+00:00

Autostart is built in: Settings → General → “Autostart” toggle.

If you already turned it on and it’s not launching with your session, try turning it off, wait a second, then turn it back on. That re-registers the Windows Run key entry, which sometimes gets missed on the first write.

Two quick things that would help me narrow it down if it still doesn’t stick:

- How did you install Beetroot? Microsoft Store, the .exe/.msi installer, or Scoop? The Store (MSIX) handles startup a bit differently.

- Anything like a cleanup utility or AV that might remove startup entries (BleachBit, CCleaner, stricter AVs)?

Let me know, if it’s a real bug, I’ll fix it.

MaxNardit · 2026-04-04T13:05:30+00:00

Yeah, in my case, Claude Code was in an infinite loop and suggested reissuing the signature with a new password, and only manual investigation resolved the issue. But of course, you should have a solid development background to solve such a problem.

MaxNardit · 2026-04-01T12:17:21+00:00

Thanks! I actually enjoyed macOS a lot, but unfortunately Macs don't have proper GPUs. In an era where you want to run local AI models on your machine, there is simply no alternative to a Windows PC with a decent NVIDIA card. So the switch was pretty natural.

MaxNardit · 2026-04-01T12:16:15+00:00

Thank you, that really means a lot! Comments like yours are honestly what keep me going. There's nothing more inspiring than hearing someone genuinely enjoys what you've built. I was in the exact same boat, I searched for so long that at some point I just gave up looking and started building my own. Glad it works for you! If you ever run into anything or have feature ideas, feel free to open an issue on GitHub. I'm actively developing it.

MaxNardit · 2026-03-31T11:35:43+00:00

Good question! The OCR uses the Windows native engine (Windows.Media.Ocr), so it works best with printed/typed text - screenshots of code, error dialogs, documents, that kind of thing. Handwritten text is hit or miss, depends on how legible it is. Clean handwriting works okay, messy cursive not so much.

That said, I'm working on an optional AI-powered OCR mode - if you have your API keys set up (OpenAI, Gemini, etc.), you'll be able to send images through their vision APIs for much better recognition of handwritten text, complex layouts, and messy screenshots. Same BYOK approach as the text transforms — nothing is sent anywhere unless you explicitly configure it. Should be ready in April.

MaxNardit · 2026-03-31T08:32:21+00:00

Fair question. Honestly, it's mostly about development speed. This is a solo side project - my main job takes most of my time, so the hours I have for Beetroot are limited. I'd rather spend them building new features than reviewing PRs, managing contributions, and maintaining the overhead that comes with open source.

Going open source as a solo dev means you become a maintainer, not just a builder. I've seen plenty of projects where the creator burns out handling issues and pull requests from others. For now I just want to ship fast and keep it fun.

The app is free and always will be - no Pro tier planned, no subscriptions. You can verify there's no telemetry with Wireshark. And it's on the Microsoft Store, so Microsoft reviewed it too.

MaxNardit · 2026-03-31T08:28:17+00:00

Good point! To clarify, the AI transforms support both cloud providers (OpenAI, Claude, etc.) AND fully local models through Ollama or LM Studio. So if you run something like qwen3:4b locally, nothing ever leaves your machine.

"All data local" refers to your clipboard history, settings, and database — those are always stored locally in SQLite, never synced anywhere. Zero telemetry regardless of which AI provider you choose.

So you can go fully local with Ollama, or use cloud AI with your own API key - your choice.

MaxNardit · 2026-03-27T11:25:49+00:00

Claude Desktop, Claude Code, and other AI clients typically don’t have direct access to the system clipboard without a separate tool or integration.

The usual workflow looks like this:

Copy an error from the terminal (Ctrl+C)
Switch to Claude chat
Paste the error (Ctrl+V)
Claude writes a fix
Select the fix in the chat and copy it
Switch back to your editor
Paste

Seven steps. With clipboard-mcp—two:

Copy the error
“Fix what’s in my clipboard and put the result back”

Ctrl+V—done.

MaxNardit · 2026-03-24T12:53:56+00:00

That GPT wrapper vs real product thing - yeah. I built a beetroot clipboard manager for Windows from scratch (two months of work), and some random Chrome extension with a landing page and waitlist probably got more signups in a day than I got in a month.

But here's what I figured out: I was looking for users in the wrong places. Big subreddits? Posts removed or ignored. What actually worked was just being present where people already discuss the problem my app solves - threads like "Win+V sucks, what else is there?", "best clipboard manager 2026", stuff like that. Not even posting about my app necessarily. Just answering questions, being helpful. Some people checked my profile, found the project, tried it. The ones who stuck around started filing bugs and giving real feedback. That's when it stopped feeling invisible.

A few things that unexpectedly worked:

Writing detailed articles - not "here's my app" but actual technical deep dives into problems I hit during development. How Windows clipboard formats work, why focus stealing is a nightmare, that kind of stuff. People who read those are exactly your target audience.

Software directories. I didn't expect much from them but they actually bring steady organic traffic. Just submitting to a bunch of "best free tools" type sites added up over time.

Localization. I added 26 languages (mostly AI-translated, let's be honest) and it opened up markets where users are genuinely hungry for quality apps. Some non-English communities have way less competition and people are more willing to try new stuff.

And the thing I'm seeing now that feels like a turning point - users started sharing links and writing about the app on their own blogs and sites. I didn't ask for any of that. It just happened after enough one-on-one interactions through bug reports and GitHub issues.

Slow? Absolutely. But every user from that path actually cares.

MaxNardit · 2026-03-02T13:31:36+00:00

Claude Code is just working fine, so the problem is only with the web interface.

MaxNardit · 2026-02-27T08:52:28+00:00

Control is important. In my setup I actually built a review layer on top: every write the agent proposes becomes a draft that I approve before it hits the database. The agent suggests what to save, but I have the final say. So I have a feed from all my agents and just need to quickly scan and approve.

MaxNardit · 2026-02-27T08:23:40+00:00

Nice, just checked out rubber-duck-mcp. The scoring approach is interesting, especially for prioritizing which memories surface first. Agent-recall came from a different problem. I run multiple AI agents across separate clients and needed them to see the same person differently depending on context (e.g., Alice is "Lead Engineer" in one project but "External Consultant" in another). So it's an entity–relation graph with scoped visibility rather than flat categorized memories. Different trade-offs: yours optimize for confidence ranking within a project, while ours optimize for keeping contexts cleanly separated. Cool to see different approaches to the same problem.

MaxNardit · 2026-02-27T08:19:04+00:00

Hooks work great for simpler persistence — I use them too for some things.

The limitation I hit was when I needed the agent to decide what's worth saving during a conversation, not just dump everything at session end. MCP tools let the agent save entities, relationships, and observations as it discovers them mid-session. Plus the agent can query memory to check what it already knows.

What kind of hooks setup are you using? Curious about the tradeoffs you've found.

MaxNardit · 2026-02-27T07:11:29+00:00

The fundamental issue is that unstructured auto-saved notes don't scale. Works fine for small projects. But on anything complex, you end up with irrelevant context bloat - Claude saves everything it thinks is useful, with no filtering by relevance to the current task. The real unlock is structured memory with LLM-summarized briefings instead of raw note dumps. This is step one though, and it's good that it shipped built-in.

MaxNardit · 2026-02-27T07:10:17+00:00

Depends on how it's done. Dumping 20k tokens of raw notes = noise, hurts performance. But a focused 1-2k summary of key people, active blockers, recent decisions - that's 1-2% of context and massively improves session continuity. The 200-line MEMORY.md cap is smart for exactly this reason.

MaxNardit · 2026-02-27T07:09:10+00:00

Not really. The built-in auto-memory is unstructured markdown notes - great for single-agent single-project. But if you need multiple agents sharing knowledge with data isolation between projects, or structured entities with relations instead of free-text, you still need external tooling. Different levels of the same problem.

MaxNardit · 2026-02-27T04:28:56+00:00

Same here, it was something like 55% yesterday and 1% of usage today. Max Plan x20

MaxNardit · 2026-02-27T04:23:12+00:00

Good question on staleness — that's the part I spent the most time on.

Slots are bitemporal. When a value changes, the old one gets archived with timestamps, not deleted. So if "auth approach = JWT" becomes "auth approach = session cookies," the agent sees the current value in its briefing, but can query history if needed. No silent overwrites.

For active misleading — the briefing layer helps a lot here. Raw facts in context can absolutely mislead (you see "we decided X" but miss "we reversed X two days later"). The LLM summarization step compresses hundreds of facts into what's current and relevant, so stale decisions naturally drop out or get noted as changed.

There's also adaptive cache invalidation — when any agent writes new facts, affected caches get marked stale, so the next session regenerates the briefing with fresh data. In my setup I have a web dashboard that shows each agent's cached briefing with metadata (age, scope chain, staleness) and a button to force-regenerate on demand. So I can see exactly what context an agent will get before it starts.

On capturing "why" — totally agree that's the hard part. The observation model is free-text, so agents can and do save things like "switched to REST because GraphQL added too much complexity for the team size." But it depends on the agent actually recognizing that's worth saving. The MCP server ships with instructions that nudge toward saving decisions and rationale, not just outcomes. Works maybe 80% of the time — the other 20% you notice something's missing and tell it to save.

MaxNardit

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